The goal of the Data Science area of study is to equip students with both practical tools and theoretical knowledge to make sense of large amounts of data. We will explore popular tools being used today (i.e., Python), deep-learning libraries, advanced probabilistic-reasoning tools and distributed-computation systems (i.e., Sparks).
This area of study will integrate faculty expertise from Electrical and Computer Engineering, Computer Science and Computational Medicine.
The curriculum will focus on unifying statistics, data mining and analysis, machine learning, and distributed and parallel systems to understand and analyze large amounts of data. During the lectures, students will learn various theoretical and algorithmic computational models and theory. They will then use these tools to analyze real-world data for their projects.
“This is the era of big data. So much data is being generated from everywhere, and the knowledge and ability to leverage that data and make sense of it will be highly desirable by industry.”
Area Director: Prof. John Cho
COM SCI 141/EC ENGR 144
(Instructor: Prof. M. Sarrafzadeh)
COM SCI 261/EC ENGR C247 Deep Learning
(Instructors: Prof. C.J. Hsieh/Prof. Y. Cao)
COM SCI 263
(Instructor: Prof. K. W. Chang)
COM SCI 143
(Instructor: Prof. J. Cho)
COM SCI 247
(Instructor: Prof. Y. Sun)
|Engineering Professional Development Elective||Engineering Professional Development Elective|
|Engineering Professional Development Elective|
|12 Units||8 Units||8 Units||8 Units|